ESRC Centre for Population Change • Working Paper 60 • January 2015

Vernon GayleRoxanne Connelly Paul Lambert

ISSN 2042-4116

CPCcentre for population change

Improving our understanding of the key drivers and implications of population change

A review of occupation-based social classifications for social research

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ABSTRACT

This working paper is a review of issues associated with measuring occupations and using occupation-based socio-economic classifications in social science research. The review is orientated towards researchers who undertake secondary analyses of large-scale micro-level social science datasets. The paper begins with an outline of how to handle raw occupational information. This is followed by an introduction to the two main approaches to measuring occupations, and a third lesser known but intellectually innovative approach. The three approaches are social class schemes, social stratification scales and the microclass approach. International comparisons are briefly described and a discussion of intersectionality with other key variables such as age and gender is provided. We are careful to emphasise that we are not advocating the uncritical adoption of any one particular socio-economic measure over and above other alternatives. Rather we are advocating that researchers should choose from the portfolio of existing socio-economic measures in an informed and empirically defensible way and we strongly advocate undertaking sensitivity analyses. We conclude that researchers should always use existing socio-economic measures that have agreed upon and well documented standards. We strongly advise researchers not to develop their own measures without strong justification, nor to use existing measures in an un-prescribed or ad hoc manner. KEYWORDS

Measuring Occupations; Social Stratification; Social Classification; Social Class; Microclass.

EDITORIAL NOTE

Professor Vernon Gayle is Professor of Sociology and Social Statistics at the University of Edinburgh. Dr Roxanne Connelly is a Research Fellow at the University of Edinburgh. Professor Paul Lambert is Professor of Sociology at the University of Stirling. Corresponding author: Professor Vernon Gayle, Vernon.Gayle@ed.ac.uk.

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ESRC Centre for Population Change

The ESRC Centre for Population Change (CPC) is a joint initiative between the Universities of Southampton, St Andrews, Edinburgh, Stirling, Strathclyde, in partnership with the Office for National Statistics (ONS) and the National Records of Scotland (NRS). The Centre is funded by the Economic and Social Research Council (ESRC) grant numbers RES-625-28-0001 and ES/K007394/1. This working paper series publishes independent research, not always funded through the Centre. The views and opinions expressed by authors do not necessarily reflect those of the CPC, ESRC, ONS or NRS.

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ACKNOWLEDGEMENTS

We gratefully acknowledge the comments of Professor Geoff Payne, Professor John Field, Dr Alasdair Rutherford and Dr Christopher Playford. Vernon Gayle, Roxanne Connelly and Paul Lambert all rights reserved. Short

sections of text, not to exceed two paragraphs, may be quoted without explicit permission provided that full credit, including notice, is given to the source.

This Working Paper Series is edited by Teresa McGowan.

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A REVIEW OF OCCUPATION-BASED SOCIAL CLASSIFICATIONS FOR SOCIAL RESEARCH

TABLE OF CONTENTS

1. INTRODUCTION .................................................................................... 1

2. THE RATIONALE FOR OCCUPATION-BASED SOCIAL CLASSIFICATIONS ................................................................................ 2

3. STANDARDISED OCCUPATIONAL CLASSIFICATIONS ............. 5

4. OCCUPATION-BASED SOCIAL CLASSIFICATIONS .................... 7

5. SOCIAL CLASS SCHEMES................................................................... 8

6. SOCIAL STRATIFICATION SCALES .............................................. 13

7. ‘MICROCLASS’ APPROACHES ........................................................ 16

8. SELECTING AN APPROPRIATE SOCIO-ECONOMIC MEASURE............................................................................................... 18

9. SOCIO-ECONOMIC CLASSIFICATIONS AND RELATIONSHIPS WITH DEMOGRAPHIC STRUCTURES ........................................................................................ 18

10. CONCLUSION..................................................................................... 21

REFERENCES ............................................................................................ 23

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1. INTRODUCTION Occupations are a key element of contemporary social life and occupation-based indicators are

central to social research. This working paper is a review of issues associated with measuring

occupations and using occupation-based socio-economic classifications in social science

research. Theoretical and empirical research on social inequalities or ‘social stratification’ has

been one of the hallmarks of UK sociology since it burgeoned as an academic discipline after the

Second World War (Pevalin and Rose 2002). Central to this field is the recognition that the

occupational structure is an important foundation for the main dimension of social stratification

(Blau and Duncan 1967). Within sociology there is a longstanding recognition that in

industrialised societies occupations are the most powerful single indicator of levels of material

reward, social standing and life chances (Parkin 1971).

Despite the sociological consensus that occupations are central to understanding social

stratification, there is no single obvious and agreed upon way of measuring occupations. The

discipline has produced a wide range of measures which are often linked with mainstream

sociological theories and concepts, most notably related to ‘social class’, and debates have been

exacerbated because of the complex nature of the occupational structure in contemporary labour

markets. The theorisation, measurement and operationalisation of occupation-based measures

have received great attention in the specialised field of social stratification research. The use and

interpretation of occupational classifications has received less attention in other areas of

sociology and in neighbouring social science disciplines, despite the centrality of occupations for

many empirical analyses. It is our conjecture that appropriately measuring occupations and using

occupational information in analyses requires thought and in-depth knowledge of these

measures. The aim of this review is to document issues associated with utilising occupation-

based social classifications in social research, and to provide some clear prescriptions for data

analysts who are not experts in this field.

We begin with an outline of how to handle raw occupational information. This is

followed by an introduction to the two major approaches to measuring occupations, and a third

lesser known but intellectually innovative approach. The three approaches are social class

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schemes, social stratification scales and the microclass approach. In addition, occupation-based

measures for international comparisons are briefly described. We conclude with a discussion of

the intersectionality of other key variables such as age and gender, and discuss a range of issues

related to the inclusion of these measures in social science analyses.

2. THE RATIONALE FOR OCCUPATION-BASED SOCIAL CLASSIFICATIONS

The most common justification for using occupation-based socio-economic measures is that they

make reliable, parsimonious indicators of the social positions of individuals (Parkin 1971; Rose

and Pevalin 2003). To most sociologists, occupation-based socio-economic measures do not

simply act as a proxy where income data themselves are unavailable, they are measures designed

to help us better understand fundamental forms of social relations and inequalities to which

income is merely epiphenomenal (Rose and Pevalin 2003). Empirical inquiries using repeated

contacts data have convincingly shown that there is a high degree of income churning from year

to year which makes income data unlikely to consistently represent individuals’ positions in

industrial economies (Jarvis and Jenkins 1997, Jenkins and Van Kerm 2009). By contrast

occupation-based socio-economic measures are more stable and therefore better describe lifetime

earnings profiles (Rose and Pevalin 2003, Goldthorpe and McKnight 2006).

Hauser and Warren (1997) contend that the social sciences have been suffering from a

preoccupation with measures of income and poverty. This focus possibly stems from the

assumed utility of monetary measures for impact or ‘real world’ relevance. The focus on income

might also reflect the relative disciplinary esteem of the field of economics within the social

sciences. Bourguignon (2006) and Goldthorpe (2012) both assert that it is possible that this

economic focus may have diverted some social scientists from major and consequential

dimensions of social inequality which are not captured by focusing purely on income.

It is necessary to note at this point that some contemporary sociologists dispute the

continued relevance of occupation-based social classifications. Against the backdrop of a vast

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quantity of empirical results charting continued class-based inequalities (e.g. Erikson,

Goldthorpe et al. 1979, Goldthorpe, Llewellyn et al. 1980, Goldthorpe, Llewellyn et al. 1987,

Erikson and Goldthorpe 1992, Wright 1997), a parallel stream of literature has claimed that

‘class as a concept is ceasing to do any useful work’ (Pahl 1989) or is indeed ‘dead’ (e.g. Holton

and Turner 1989, Clark and Seymour 1991, Kingston 1994, Joyce 1995, Lee and Turner 1996,

Pakulski and Waters 1996). These theories generally argue that the lives and experiences of

individuals in modern society are too fluid and transient, and too influenced by the processes of

globalisation, to fit neatly within class categories. Pakulski and Waters’ (1996) account of the

‘death of class’ centres on two main ideas. First, that class based divisions peaked in industrial

society and have been declining ever since. Second, that although there are inequalities in

modern society these are not aligned with traditional social classes.

There are however a number of weaknesses in the ‘end of class’ thesis. Goldthorpe and

Marshall (1992) note that the concept of class which is being attacked is a concept which is

never clearly defined, is most aligned to the Marxist tradition, and which differs greatly from the

more recent sociological concepts of social class that have been developed and deployed in

empirical studies. The more nuanced aspects of the concept of social class are largely overlooked

by those who argue that class is dead, and social class is often represented in a caricatured and

simplistic manner (Goldthorpe and Marshall 1992). Platt (2011, pp.15) highlights that central

concerns in contemporary class analysis include the notion of changes in the influence of class,

the declining importance of class, and the intersectionality of other variables such as gender and

ethnicity. We would add that a central theme in contemporary class analysis is the study of the

extent to which the influence of social class has decreased over time in relation to major

economic and social changes (e.g. Shavit and Blossfeld 1993, Hochstadt 1999, Breen 2004,

Zijdeman 2009, Erikson and Goldthorpe 2010).

Whilst class analysts have researched these issues in some depth, Goldthorpe and

Marshall (1992) assert that sociologists who claim that class is dead have provided little

convincing evidence to support their arguments. Furthermore, Goldthorpe and Marshall (1992)

have also noted that there has been no attempt to provide longitudinal evidence of change in the

nature or influence of class to provide adequate support for the ‘death of class’ argument. It is

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also notable that many theoretically oriented sociologists, from very different standpoints, have

continued to describe the importance and relevance of class in contemporary society (Giddens

1981, Skeggs 1997, Sayer 2005).

Savage et al. (2013) recently proposed a new model of social class based on sociological

theory advanced by the French sociologist Bourdieu. From this theoretical standpoint

occupations are not the main indicators of social stratification positions. This work has led to an

extensive amount of discussion over the last year. Savage et al. (2013) have developed a new

model of class based on the concepts of economic capital (e.g. income and wealth), cultural

capital (e.g. engagement with cultural goods and activities), and social capital (e.g. social

contacts and networks) (see Bourdieu 1984). These concepts are measured using indicators such

as household income, savings, property value, the number of social contacts held and the

occupations of these social contacts, engagement with ‘highbrow culture’ (e.g. visiting museums

or listening to classical music) and engagement with ‘emerging cultural capital’. Emerging

cultural capital describes activities once considered ‘lowbrow culture’ but that may now be more

ubiquitous. Examples might include using social networking websites, going to a gym, or on-line

gaming.

Rather than theorising occupations as the main basis of the opportunity structure,

Bourdieu (1984) argues that the three capitals can be used to better explain the processes of

social reproduction. Based on this theory Savage et al. (2013) contend that by measuring

individuals’ levels of these capitals, a far more informative social class scheme can be developed

than the traditional occupation-based measures that are widely used. Payne (2013) has noted that

the seven ‘new’ classes proposed by Savage et al. (2013) are very similar to the established UK

National Statistics Socio-Economic Classification categories, but with the added distinction of an

‘elite’ category. Therefore this new scheme does not represent a revolutionary re-working of the

established social class categories. The measure proposed by Savage et al. (2013) has been

extensively critiqued (Lambert and Griffiths 2013, Mills 2013, Payne 2013, Rose and Harrison

2013, Bradley 2014, Mills 2014, Rollock 2014). Despite the critiques offered by the ‘end of

class’ theorists, and the direction proposed by Savage et al. (2013), there is no strong empirical

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evidence that dissuades us of the extremely high value of using existing occupation-based

measures in the secondary analysis of large-scale social surveys.

3. STANDARDISED OCCUPATIONAL CLASSIFICATIONS Modern industrial economies are characterised by a wide range of occupations each of which

contain numerous different jobs. Collecting job related information (e.g. job titles) is routinely

the first step in collecting occupational information within social surveys. The next step is

usually marshalling this information into a recognised occupational scheme.

The raw occupational information in large-scale micro-level social surveys is usually

coded into a standardised occupational unit group scheme. In the UK, it is common for social

survey data to be coded into the Office for National Statistics Standardised Occupational

Classification, which are known as SOC codes (see Office for National Statistics 2010). Some

surveys are coded into the International Labour Organisation’s International Standard

Classification of Occupations, which are known as ISCO codes (see Ganzeboom 2010). This

information is often augmented with additional employment data such as employment status (e.g.

self-employed or supervisory). Occupational unit group codes are produced by matching original

textual occupational descriptions (e.g. from survey question responses) with a standardised list of

occupations. We consider that it is extremely important that data collectors maintain

occupational data in the form of an established protocol (e.g. using SOC codes). Coding raw

occupational data (e.g. textual descriptions) directly into socio-economic measures is highly

unsatisfactory because it will result in the loss of valuable detailed occupational information. As

Lambert (2002) demonstrates without the use of an established protocol for coding raw

occupational information (e.g. using SOC codes) it is later impossible to test for comparability

between both current and future occupation-based measures.

Translating raw survey data into unit group codes can be a time consuming exercise, but

the burden is greatly reduced through the use of computer assisted and computer automated

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coding procedures (Elias, Halstead et al. 1993). The Computer Aided Structured Coding Tool1

(CASCOT) is an online resource for the quick and reliable coding of occupational descriptions,

which was developed by the Institute of Employment Research at the University of Warwick

(Jones 2004). The CASCOT program compares the text in the description of an occupation with

the text in standardised descriptors for occupational classifications. The software then presents a

list of recommended matches. CASCOT also provides a score for the matches indicating the

degree of certainty that the given occupational code is correct. The Office for National Statistics

also publish an open access online coding tool2 that operates in a similar manner, and these and

other coding software are available as off-line packages suitable for bulk-processing large

volumes of data.

Schemes of unit group codes are updated periodically and the current nationally specific

UK scheme is SOC20103. Another example of a standardised occupational code is the

International Standard Classification of Occupations ISCO4 (International Labour Organization

2010). ISCO is also widely used in both cross-national and nationally specific survey datasets

(Bergman and Joye 2005). ISCO represents an important effort to develop internationally

comparable occupational codes, which facilitate cross-national comparisons in social surveys

(Elias 1997). In many countries, cross-walks’ are available, that enable values of the national

standardised occupational unit group scheme (e.g. SOC2010) to be translated into ISCO5. These

cross-walks are usually written by researchers and/or by national statistics agencies.

1 CASCOT can be accessed here: http://www2.warwick.ac.uk/fac/soc/ier/software/cascot/. 2 The Office for National Statistics coding tool is available here: http://www.neighbourhood.statistics.gov.uk/HTMLDocs/dev3/ONS_SOC_occupation_coding_tool.html. 3 Although SOC2010 is the most up to date UK scheme, some surveys may use coding guidelines that are based on previous schemes such as SOC2000, SOC90 or CO80. Further details of SOC2010 are available here: http://www.ons.gov.uk/ons/guide-method/classifications/currentstandardclassifications/soc2010/index.html. 4 Further details of ISCO are available here: http://www.ilo.org/public/english/bureau/stat/isco/isco88/index.htm. 5 Exemplary resources are available on the webpages of Professor Harry Ganzeboom: http://www.harryganzeboom.nl/ismf/ismf.htm.

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4. OCCUPATION-BASED SOCIAL CLASSIFICATIONS Organising occupational information into standardised occupational codes is only the first stage

in the process. Secondary data analysts will generally not need to engage with this stage of the

process when they are using existing survey resources that are well curated. The British

Household Panel Survey, is a prime example of a large-scale social survey with well curated

occupational data, that is readily available for secondary data analysis (Taylor, Brice et al. 2010).

The second stage in the process of developing occupation-based social classifications is to

convert these data from standardised lists of occupational unit groups (e.g. SOC codes) into the

required socio-economic measure.

The means to convert unit group codes and employment status data into occupation-based

measures is typically supplied in a listing of occupational unit groups alongside the

corresponding levels of an occupation-based measure. This may take the form of a table, textual

description, statistical software command file, or a matrix of data for matching (see Lambert and

Bihagen 2012 for a more extended description). In order to carry out these operations the

researcher will usually require some skills in the use of statistical software for data management

(see Treiman 2009, Mitchell 2010).

Lambert and Bihagen (2014) report that there has been a great deal of inconsistency in

how researchers have organised occupational information into occupation-based social

classifications. For instance they claim that upwards of a thousand different measures based upon

occupations have been used in contemporary social science. It is unsurprising that this surfeit of

measures may initially seem daunting for researchers, especially those that are not social

stratification specialists. It is notable that many social classifications emerge from very different

sociological theories, which influence both their conceptual and empirical foundations.

It is well observed that in many secondary analyses of large and complex social surveys

the analysts tend to select a single occupation-based measure. The choice of measure might be

made as a result of theoretical fiat, or due to more practical operational issues, or even a mixture

of both. Our methodological advice is that researchers should utilise existing measurement

options whenever possible, and should avoid producing their own measures, or using existing

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measures in an ad hoc manner, unless they have very strong and clearly justified reasons. Social

survey methodologists have heralded this same dire warning on several previous occasions

(Blumer 1956, Stacey 1969, Burgess 1986, Bulmer, Gibbs et al. 2010, Lambert and Bihagen

2014).

Our advice is based on the following premises. First, the field of stratification research is

highly specialised and a great deal of theoretical thought and empirical testing has been directed

towards the development of occupation-based measures. Therefore it is probable that a measure

suitable for most analyses already exists. Second, the adoption of an existing measure is almost

always more time efficient. Third, and most importantly in our view, existing measures have

agreed and documented standards, and therefore facilitate replication. As Lambert and Bihagen

(2012) assert this locates firmly within the culture of cumulative social scientific endeavour.

5. SOCIAL CLASS SCHEMES Social Class schemes are very widely used in sociological research. Social class schemes can

generally be regarded as socio-economic measures that divide the population into unequally

rewarded categories (Crompton 2008). Social class schemes are not necessarily hierarchical,

although an ordinal structure is often evident (Glass 1954, Carlsson 1958). There are a plethora

of social class schemes, and these measures are often informed by different theoretical

standpoints (see Crompton 2008). Wright (2005) distinguished between groups of social class

measures which could be classified as being either Marxist6, Weberian or Durkhiemian in their

approach. As we have described above, more recently the measure proposed by Savage et al.

(2013) has its genesis in the theoretical work of Bourdieu. However, Marxist and Bourdieusian

socio-economic measures are not usually readily derived from occupational information alone,

and do not ordinarily feature in social survey datasets.

6 Readers might be interested to know that Marxist class schemes generally consider social relations of economic production as the basis upon which class groups can be defined, rather than the ‘technical’ divisions of labour (i.e. occupations) (see Wright and Perrone 1977, Wright, Costello et al. 1982, Wright and Martin 1987, Wright 1989, Wright and Cho 1992, Western and Wright 1994, Wright 1997, Wright 2005).

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Many of the earliest published social class schemes focussed upon differences in the skill

levels of occupations, and defined social categories in those terms. Skill categories were

sometimes calculated based on typical qualification requirements, but their identification was

also often associated with evaluations of the relative prestige or social standing of the

occupation. A prominent example is the UK’s long standing ‘Registrar General’s Social

Classification’ (e.g. Szreter 1984). There is evidence that skill based measures are empirically

very powerful, and they remain a popular choice in social research (see Elias and McKnight

2001, Tahlin 2007).

The work of John Goldthorpe has arguably generated the most influential social class

scheme in sociology and allied disciplines (Evans 1992). The Erikson-Goldthorpe-Portocarero

(EGP) scheme (see Erikson, Goldthorpe et al. 1979) has become a widely used measure in social

research (Evans 1992). The theoretical principles of the EGP approach led to the development of

subsequent cognate schemes, including CASMIN (Erikson and Goldthorpe 1992), the UK’s

National Statistics Socio-Economic Classification (Rose and Pevalin 2003) and the European

Socio-Economic Classification (Rose and Harrison 2007). In this tradition, employment relations

in the labour market are held to be of key importance to the allocation of individuals into social

class categories (Erikson and Goldthorpe 1992). Individuals within a social class are considered

to share similar ‘market situation’ (e.g. levels of income, economic security, chances for

economic advancement) and ‘work situation’ (e.g. authority and control) (Goldthorpe 1980).

Accordingly, those individuals within a social class are thought to hold similar life chances and

often lifestyles.

In its least aggregated form the EGP scheme identifies eleven classes, although a seven

class version is widely used in empirical analyses (see Table 1). Erikson and Goldthorpe (1992)

recommend that researchers move between the seven, five and three class versions based on the

need to balance explanatory comprehensiveness with explanatory parsimony. They state that the

scheme could be extended to include more classes if there was good reason to do so (Erikson and

Goldthorpe 1992). The use of varied forms of the EGP scheme is consistent with the claim that

the measure is an instrument du travail rather than a definitive representation of social class

groupings in the UK (Erikson and Goldthorpe 1992).

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In 1994 the UK Economic and Social Research Council (ESRC) commissioned a review

of social classifications. As a result of this review, the EGP approach was adopted as the basis of

a new government measure of social class (Rose 1995, Rose and Pevalin 2003). Consequently,

the National Statistics Socio-Economic Classification (NS-SEC) was developed, and since 2001

this occupation-based measure, described in Table 2, has been used in official statistics and

government research in the UK (Office for National Statistics 2010). In congruence with the

EGP approach, the NS-SEC approach comprises of aggregate groupings of individuals who are

considered to share similar life chances and lifestyles. Similarly, various reduced versions of the

scheme are recommended (see Table 2).

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Full Version Collapsed Versions Seven-class version Five-class version Three-class version

I Higher-grade professionals, administrators and officials; managers in large industrial establishments; large proprietors

I+II Service class: professionals, administrators and managers; higher-grade technicians; supervisors of non-manual workers

I-III White-collar workers

I-III+ IVa+b

Non-manual workers

II Lower-grade professionals, administrators and officials; higher-grade technicians; managers in small industrial establishments; supervisors of non-manual employees

IIIa Routine non-manual employees, higher grade (administration and commerce)

III Routine non-manual workers: routine non-manual employees in administration and commerce; sales personnel; other rank-and-file service workers

IIIb Routine non-manual employees, lower grade (sales and services)

Iva Small proprietors, artisans, etc., with employees

IVa+b

Petty bourgeoisie: small properties and artisans, etc., with and without employees

IVa+b Petty bourgeoisie IVb Small proprietors, artisans, etc., without

employees IVc Farmers and smallholders; other self-

employed workers in primary production IVc Farmers: farmers and small holders

and other self-employed workers in primary production

IVc+VIIb

Farm workers

IVc+VIIb

Farm workers

V Lower-grade technicians; supervisors of manual workers

V+VI

Skilled workers: lower-grade technicians; supervisors of manual workers, skilled manual workers

V+VI Skilled workers

V+VI+ VIIa

Manual workers

VI Skilled manual workers VIIa

Semi-skilled and unskilled manual workers (not in agriculture, etc.)

VIIa Non-skilled workers: semi and unskilled manual workers (not in agriculture, etc.)

VIIa Non-skilled workers

VIIb

Agricultural workers and other workers in primary production

VIIb

Agricultural labourers: agricultural and other workers in primary production

Table 1: Thorpe class Scheme (Erikson & Goldthorpe, 1992, pp. 38-39).

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Eight-class version Five-class version Three-class version 1 Higher managerial,

administrative and professional occupations

1 Higher managerial, administrative and professional occupations

1 Higher managerial, administrative and professional occupations

1.1 Large employers and higher managerial and administrative occupations

1.2 Higher professional occupations

2 Lower managerial, administrative and professional occupations

3 Intermediate occupations

2 Intermediate occupations

2 Intermediate occupations

4 Small employers and own account workers

3 Small employers and own account workers

5 Lower supervisory and technical occupations

4 Lower supervisory and technical occupations

3 Routine and manual occupations

6 Semi-routine occupations

5 Semi-routine and routine occupations

7 Routine occupations 8 Never worked and

long-term unemployed

Table 2: The National Statistics Socio-economic Classification (NS-SEC) In a recent and related exercise, a ‘European Socio-Economic Classification’ (ESeC)

has been developed (Harrison and Rose 2006, Rose and Harrison 2010). This is a

social class scheme, based upon the EGP model, which is designed to facilitate cross-

nationally comparative research. ESeC7 comprises a nine-class categorical measure,

with recommended reduced versions of five or three classes, which can be readily

operationalised from data coded into the three-digit version of the ISCO occupational

unit group scheme. The ‘ESeC’ scheme is specifically designed for international

research, although other UK oriented versions of the EGP scheme have also been

exploited in cross-nationally comparative studies (e.g. Erikson and Goldthorpe 1992,

Ganzeboom 1996, Breen 2004, Blossfeld and Hofmeister 2005).

7 Full details of the ESeC scheme are available here: https://www.iser.essex.ac.uk/archives/esec/user-guide.

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The EGP social class scheme and its derivatives are widely used in British

sociology, and several studies have provided evidence of acceptable construct8 and

criterion9 validity for this measure (e.g. Evans 1992, Evans and Mills 1998, Evans

and Mills 2000). Nevertheless, these social class schemes have also been evaluated

critically. Questions have been asked about the degree of within-class homogeneity in

social class categories, and concerns have been raised that individuals placed within

the same social class can hold very different positions within social hierarchies

(Blackburn and Prandy 1997, Bergman and Joye 2005). Penn (1981), Hout and

Hauser (1992) and Blackburn and Prandy (1997) have also argued that the EGP

scheme’s categories downplay the key element of hierarchy in social stratification.

Lastly, measures from the EGP scheme have a relatively a high number of categories,

so it is sometimes suggested that they are not well suited to incorporation in

multivariate statistical analyses (e.g. regression models) because having a large

number of categories inhibits the estimation of interaction effects. A consequence is

that this limitation might encourage researchers, de facto, into the less desirable

practice of simplifying the measure into a more coarse-grained format.

6. SOCIAL STRATIFICATION SCALES Having introduced categorical social class schemes we now turn our attention to

occupation-based scales. The principal difference between categorical social class

schemes and stratification scales is that rather than placing individuals into

qualitatively distinct categories, social stratification scales place individuals at some

point on a continuous or gradational one-dimensional hierarchy (Bergman and Joye

2005). This single dimension has often been labelled ‘status’, but more generally

reflects ‘relative social advantage’ (Jonsson, Grusky et al. 2009). Scaling approaches

also, technically, accommodate measurements that assign scores to occupations in

more than one dimension (e.g. Levine and Spadaro 1988), but in practice scales are

only regularly used in a single dimensional framework in applied research.

8 Construct validity is based on the assessment of whether a measure reflects the underlying construct of interest (Cronbach and Meehl 1955). 9 Criterion validity is based on the assessment of whether a measure behaves in the expected fashion, given the theory underlying the measure (Carmines and Zeller 1979)

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A notable example of a social stratification scale is the Cambridge Social

Interaction and Stratification Scale (CAMSIS) (Stewart, Prandy et al. 1980, Prandy

1990). This in an empirically constructed scale that is based on the theoretical idea

that there is a stratification order derived from a hierarchical structure of advantage

(and disadvantage) arising from the unequal distribution of social, cultural and

economic resources. In the CAMSIS approach, individuals are embedded in social

networks of relationships within which they engage in social, cultural, political and

economic interactions. These social interactions are circumscribed by the social

distance of these social actors. The idea of the centrality of ‘social space’ is not

unique to the CAMSIS approach, and has a long history in the sociological

literature10. Chan (2010) describes a more recent project that constructed occupation-

based scales by analysing social interaction patterns using a very similar approach to

the CAMSIS perspective.

The CAMSIS approach is based on examining patterns of social interaction

between occupations that are uncovered by examining the frequency of links between

people in different occupations. These links are typically defined either by friendship

or by marriage/cohabitation. CAMSIS scales are formed using statistical analysis of

‘dimensions’ within the social interaction structure11 (Prandy 1999). In this approach,

country and time specific scales are usually calculated, and different CAMSIS scales

can also be generated for men and women, and could also be generated for other

important socio-demographic differences if desired (e.g. ethnic groups or

geographical regions). Lambert et al. (2008) conclude that this quality of ‘specificity’

has attractive properties for a wide range of analyses where more detailed resolution

might be illuminating.

Two other important stratification scales are the Standard International

Occupational Prestige Scale (SIOPS) (see Ganzeboom and Treiman 1996,

Ganzeboom and Treiman 2003) and the International Socio-Economic Index (ISEI)

(Ganzeboom, De Graaf et al. 1992). SIOPS is devised by taking survey information

on prestige ratings given by respondents to samples of jobs, and calculating averages 10 For example Sorokin (1927) states that ‘man’s social position is the totality of his relationships towards all groups of a population, within each of them, towards its members’. 11 Detailed guidance for the translation of occupational codes and employment status information into CAMSIS measures can be found on the project’s website: http://www.camsis.stir.ac.uk/.

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within and across societies (Treiman 1977). Treiman’s original analysis compared

ratings from over sixty societies, and drew the important conclusion that variation

from society to society, and across time, in the prestige allocated to occupations was

minimal. This axiom is often referred to as the ‘Treiman constant’ within stratification

research12 (Hout and DiPrete 2006). SIOPS provides a hierarchical ranking from the

least to the most esteemed occupations according to average ratings, and scores are

shown to correlate strongly with the socio-economic circumstances of individuals

who hold these occupations. ISEI by contrast calculates scores for occupations based

upon their average profiles in terms of the income and educational qualifications held

by their incumbents (with some adjustment for age profiles). Further examples of

social stratification scales include scales based only upon the average income of

occupations (e.g. Sobek 1995), upon career prospects in terms of average wage

growth (e.g. Bihagen and Ohls 2004), or upon job quality or desirability (e.g. Jencks,

Perman et al. 1988, Mills 2007).

As continuous measures, all of the occupation-based social stratification scales

lead to numeric values being attached to occupations, but the relative importance of a

specific value is only meaningful in comparison with other occupations on the same

scale. For example, the CAMSIS scales are usually standardised to a mean of 50 and

standard deviation of 15 in each version, but the SIOPS and ISEI measures are scaled

in terms of their original measurement and they typically have a mean of around 40,

and a standard deviation of about 14, in a nationally representative sample. CAMSIS

scales tend to be specific to particular societies whereas ISEI and SIOPS are designed

to be ‘universal’ (i.e. the same scores are applicable to the same occupations across

different societies). However, socio-economic index and prestige scales can also be

calculated ‘specifically’ within a society, and a ‘universal’ version of the CAMSIS

scale is also available (De Luca, Meraviglia et al. 2010). 12 Coxon and Jones (1978, 1979, 1979) have critiqued Treiman’s approach at length, based on analyses which focus on the cognitive issues involved when asking individuals to rank occupations. Coxon and Jones considered the types of distinctions people might draw between occupations. They experimented with tasks such as asking respondents to sort occupations into groups and asking the respondents to describe the criteria by which occupations could be ordered. They argue that these evaluative tasks should have preceded Treiman’s protocol to rank the occupations by prestige, in order to ensure the respondents had a clear basis by which to rank the occupations (Coxon and Jones 1978). Coxon and Jones (1978, 1979, 1979) also present evidence that the ranking of occupations can vary between individuals and groups which may be overlooked when producing average rankings or scales. For example, individual’s exhibit a pattern of ‘occupational egoism’ whereby they give more favourable ratings to their own occupations and occupations similar to their own (Coxon and Jones 1978).

16

A major attraction of all scale-based socio-economic measures is their relative

parsimony in statistical analyses. Scales typically need only a single parameter (i.e.

for a linear effect) or two parameters (e.g for a curvilinear effect) to summarise their

influence in a statistical modelling approach (for example a standard regression

model). In many circumstances this parsimony offers a major improvement over the

inclusion of a categorical social class scheme. In statistical analyses where the socio-

economic measure may interact with other explanatory variables included in the

analysis the parsimony that emerges from using a scale is further emphasised.

Another important attraction of using stratification scales as occupation-based

measures is that their numerical functional form lends them to arithmetic

standardisation strategies that may aid comparative evaluations of social change. For

instance, in some analyses in stratification research it is common practice to apply

mean-standardisation to scale scores within the country or year of a particular dataset.

Subsequent results such as association statistics are expressed in terms of their relative

influence in each context. It is well-known in stratification research that similar

comparisons are much harder to conduct when categorical occupation-based measures

are used. This is because there are often substantial changes in the distribution of

cases to occupation-based categories over time or between countries.

7. ‘MICROCLASS’ APPROACHES Grusky and colleagues have provided a very powerful critique of traditional social

class schemes which has led to their development of the 'microclass' approach

(Grusky and Sørensen 1998, Grusky and Sørensen 2001, Grusky and Weeden 2006).

This novel perspective suggests that the categorical approach of class schemes is

desirable, but that there are many other important divisions than are conventionally

demarked in ‘big class’ schemes such as the EGP scheme. ‘Big class’ schemes

generally feature a modest number of social class categories (e.g. nine in EGP). These

‘big classes’ contain a large number of occupations, for example there are eighty eight

occupations (measured by SOC90) in the Semi-Routine Occupations Category (NS-

SEC 6) of the UK National Statistics Socio-Economic Classification. By contrast, the

17

microclass approach defines a much larger number of classes based upon

institutionalised occupational divisions. Microclass schemes typically feature around

eighty to one hundred different classes. Grusky and Sørensen (1998) contend that

traditional social class schemes fail to represent detailed social structures within big

classes, and that the social structure is not adequately represented by uni-dimensional

hierarchical scales.

Details of the microclass scheme can be found on the microclass project

homepage13 (see also Grusky, Jonsson et al. 2012). Theoretically, microclasses are

defined by the social and/or technical institutionalisation of occupations (e.g.

plumber, baker, doctor) rather than agglomerate classes (e.g. skilled manual workers,

or professionals). In practice however, the empirical operationalisation of

microclasses usually results in some groups being more homogeneous and more

clearly institutionalised than others. Nevertheless a major attraction of the microclass

approach is that it facilitates the investigation of potentially important substantive

differences at the detailed occupational level that may be hidden within the large

categories of ‘big’ social class schemes.

The microclass approach is still comparatively new, but it has been

successfully employed to study both social mobility and educational inequalities

(Jonsson, Grusky et al. 2009, Gayle and Lambert 2011). Despite the appeal of the

microclass approach it has been subject to theoretical critique (see Erikson,

Goldthorpe et al. 2012). Erikson et al. (2012) argue that the disaggregation of

categories mean that patterns linked to microclasses cannot be clearly interpreted

within the theoretical framework that is useful to a ‘big class’ measure. At a practical

level the inclusion of a large multiple category explanatory variables tends to decrease

parsimony in standard statistical models. In addition, to ensure adequate statistical

power when working with survey data covering the large number of microclass

categories, large sample sizes are required. We have stated above that compared with

categorical measures, scales better facilitate analyses that include interaction effects,

and we therefore note that this issue will be amplified with a categorical variable with

a large number of categories.

13See: http://www.classmobility.org/.

18

8. SELECTING AN APPROPRIATE SOCIO-ECONOMIC MEASURE

With so many different socio-economic measures available, selecting an appropriate

measure may at first appear to be a daunting task, especially for researchers who are

not social stratification specialists. We argue that a sensible and defensible solution is

to proceed by selecting several different operationalisations of the measures. We

stress that operationalising an occupation-based measure is not necessarily a simple

case of selecting one superlative measure. Therefore a good solution is to construct a

number of measures and evaluate them through a ‘sensitivity analysis’.

We use the term ‘sensitivity analysis’ to describe the process of investigating

the influence which small alterations to a statistical analysis, for example the use of

different operationalisations of an explanatory variable in a statistical model, have on

substantive results. In most circumstances a new sensitivity analysis is probably

required for each new analysis. This is because the particular features of an

occupation-based measure are likely to be varied in different analyses and, more

importantly, cannot be predicted a priori. The process of conducting a sensitivity

analysis can seem burdensome and even uninspiring, however modern software

capabilities mean that at least in principle it is now quite easy to re-run analyses using

different candidate measures. We contend that undertaking sensitivity analyses is of

considerable benefit to social science more generally as it increases rigour and

therefore adds confidence to results. In the same way as good analysts put effort into

comparing the results of different forms of statistical analysis, similar effort should be

put into comparisons of measures based on alternative key social science variables.

9. SOCIO-ECONOMIC CLASSIFICATIONS AND RELATIONSHIPS WITH DEMOGRAPHIC STRUCTURES

An important consideration when analysing occupation-based socio-economic

measures is their relationship with other key social science variables (e.g. age, gender,

and time period). In many analyses occupation-based measures will show moderate

correlations with other key variables. In some extreme cases if this is ignored there is

a danger that this may result in misleading interpretations (Prandy 1986, Lambert, Tan

19

et al. 2008). Lambert et al. (2008) explored this issue focussing on time periods,

countries and gender. They concluded that whilst temporal changes in the meaning of

occupations are slight, gender differences in occupational distributions are so

entrenched that they should be considered fundamental to the evaluation of

occupation-based measures (see also Prandy 1986, Gabriel and Schmitz 2007).

The role of age differences in occupational classifications has received far less

consideration than the effects of gender. However, several recently advocated

occupation-based measures are known to have strong associations with age (see Kunst

and Roskam 2010). Multidimensional measures of stratification, for example the

scheme recently developed using data from the Great British Class Survey (Savage,

Devine et al. 2013), also appear to be strongly linked to age differences.

In standard statistical analyses (e.g. regression models), it is reasonably

straightforward to incorporate main effects for key variables (e.g. age and gender),

and in many applications this will provide increased control for any correlation with

occupation-based measures. Following from this we strongly advise that secondary

data analysts consider including as many key variables in their analysis as

appropriate, whilst being mindful of the requirement of parsimony. Similarly, analysts

should consider exploring interaction effects between key variables. An alternative

approach to providing increased control for relationships between key variables is to

use occupation-based measures that have been derived for specific groups. Examples

of such measures include the gender-specific CAMSIS scales, or the social class

scheme for women’s jobs as recommended by Martin and Roberts (1984).

In the case of age, a general solution may simply be to control for age and age

related interactions when including occupation-based measures in statistical models.

This solution can easily be extended to incorporate nonlinear age effects. On this

topic, the concept of occupational maturity is also important. An argument expressed

by Goldthorpe et al. (1987) is that most adults reach a point of ‘occupational

maturity’, around about the age of 35, after which it is relatively unlikely they will

experience major changes in their occupational position. Analyses, particularly in the

area of social mobility for example, have often been restricted to samples of adults

over this age (see Erikson and Goldthorpe 1992). More recently, Tampubolon and

20

Savage (2012) have suggested that the appropriate age of occupational maturity may

have risen slightly over time. Secondary survey data analysts using occupational-

based socio-economic classifications should be cognisant of the concept of

occupational maturity and consider adjusting their analyses whenever it is required.

A further issue when using occupation-based measures relates to how they can

be used to represent households rather than individuals. This has been expressed in

stratification research as a debate between ‘individual’, ‘dominance’ and

‘conventional’ approaches to social classification (e.g. Erikson 1984). The individual

approach uses the individual’s current or last occupation. The ‘dominance’ approach

measures all jobs in the household and typically assigns a measure based on the

economically dominant occupation within the household. This is usually the

occupation which contributes the most to household circumstances and is generally

the one with the longest hours of work. The ‘conventional’ approach assigns a

position on the basis of the occupation of the ‘conventional head of the household’.

This is most commonly the oldest employed male living within the household.

Another alternative is simply to incorporate the necessary individual variables

that relate to occupational characteristics within the household. For example within a

statistical model of a child’s educational attainment information on both their

mother’s and their father’s occupation might be included. In practice in some analyses

such measures may be highly correlated and suitable thought must therefore be put

into the precise interpretation of these effects. We advocate that secondary analysts of

survey datasets should explore alternative model formulations, thoughtfully consider

their effects, and then suitably document alternative results.

Finally, an enduring problem when using occupational measures is the

complexity of making comparisons over time when the underlying structure of the

labour market has changed. In some secondary analyses of large-scale social surveys

where occupation-based measures are included as explanatory variables this will not

be an issue due to the restricted time-frame of the analysis. In more specialised

analyses, for example in research on inter- and intra-generational mobility, more

thought will be required regarding structural changes in the labour market. We

suggest that in such analyses specialist statistical approaches that are directed towards

21

providing increased control to help to combat this problem should be considered (see

especially Erikson and Goldthorpe 1992).

10. CONCLUSION We began this paper by stating that despite occupations being central to a broad

portfolio of substantive social science research questions there is no single agreed

upon way of measuring occupations. An aim of this paper has been to improve the

awareness that there are a number of varied occupation-based measures which can be

used in social research. We have highlighted that there are a series of issues which

require thought when including occupation-based measures in any substantive

analysis. Most importantly we strongly advise researchers not to develop their own

measures without strong justification, or to use existing measures in an un-prescribed

or ad hoc manner. Our clear recommendation is that researchers should always use

existing occupation-based measures that have agreed upon and well documented

standards.

We are careful to emphasise that we are not advocating the uncritical adoption

of any one particular socio-economic measure over and above other alternatives.

Rather we are advocating that researchers chose from the portfolio of existing socio-

economic measures in an informed and empirically defensible way. As Lambert and

Bihagen (2014) conclude, measures are often selected on theoretical grounds, which

rest on the claim that a given measure captures a specific aspect of the occupational

structure. Recent empirical endeavours which have attempted to provide sensitivity

analyses, indicate that the theoretical orientations usually ascribed to specific

occupation-based measures do not necessarily exert appreciable influence on

substantive results (see Gayle and Lambert 2011, Lambert and Bihagen 2014). We

conclude that rather than adopting a particular socio-economic measure on theoretical

grounds, the secondary data analyst should focus more attention on the analytical

benefits of competing measures. These considerations should include operational

issues such as the specific form of the socio-economic measures, how to best

maximise model parsimony, how to effectively specify models with suitable

additional key variables, and where appropriate, how best to include relevant

interaction effects.

22

We advocate that secondary data analysts evaluate the widest possible number

of socio-economic measures and make the results of these explorations available in

the form of a ‘sensitivity analysis’, for example in a data appendix. In many well

curated large-scale surveys a number of alternative measures will be deposited with

the data. Therefore sensitivity analyses can easily be undertaken. In datasets where

alternative measures are not readily available we advocate that secondary data

analysts place effort into constructing as many alternative measures as possible using

detailed standardised occupational unit group codes. The secondary data analyst must

always be cautious not to use these measures in an ad hoc manner. An obvious

example of this is combining categories of a social class measure in an unstandardised

or un-prescribed manner. We hope that this paper provides succinct information on

the foundations of existing occupation-based measures. In addition we have attempted

to provide practical advice that will make a positive contribution to how existing

socio-economic measures can be better incorporated into social science analyses.

23

REFERENCES Bergman, M.M. and Joye, D. (2005). Comparison Social Stratification Schemata: CASMSIS,

CSP-CH, Goldthorpe, ISCO-88, Treiman, and Wright. University of Cambridge, Cambridge Studies in Social Research, No10.

Bihagen, E. and Ohls, M. (2004). Towards a Definition of Dead-End Jobs: Empirically derived measures of dead-end-jobs using Swedish Data. Work, Employment and Society Conference.

Blackburn, R.M. and Prandy, K. (1997). "The Reproduction of Social Inequality." Sociology 31 (3), 491-509.

Blau, P. and Duncan, O. (1967). The American Occupational Structure. London, John Wiley and Sons.

Blossfeld, H. and Hofmeister, H. (2005). GLOBALIFE - Life Courses in the Globalization Process: Final Report. Bamberg, Germany, Faculty of Social and Economic Science, Otto Freidrich University of Bamberg.

Blumer, H. (1956). Sociological analysis and the" variable". American sociological review, 683-690.

Bourdieu, P. (1984). Distinction. London, Routledge. Bourguignon, F. (2006). “From Income to Endowments: The Difficult Task of Expanding the

Income Poverty Paradigm.” In Grusky, D.B. and Kabbur, R. Poverty and Inequality. Stanford, 76-102.

Bradley, H. (2014). Class Descriptors or Class Relations? Thoughts Towards a Critique of Savage et al. Sociology. doi:10.1177/0038038514520855.

Breen, R. (2004). Social Mobility in Europe. Oxford, Oxford University Press. Bulmer, M., Gibbs, J. and Hyman, L. (2010). “Introduction.” In Bulmer, M., Gibbs, J. and

Hyman, L. Social measurement through social surveys: an applied approach. Farnham, Ashgate: 1-9.

Burgess, R. (1986). Key Variables in Social Investigation. London: Routledge and Kegan Paul. Carlsson, G. (1958). Social Mobility and Class Structure. Lund, Gleerups. Carmines, E.G. and Zeller, R.A. (1979). Reliability and Validity Assessment. Thousand Oaks,

Sage. Chan, T.W. (2010). “The social status scale: Its construction and properties.” In Chan, T.W.

Social Status and Cultural Consumption. Cambridge, Cambridge University Press: 28-56.

Clark, T.N. and Seymour, M.L. (1991). Are Social Classes Dying? International Sociology, 6, 397-410.

Coxon, A.P.M. and Jones, C.L. (1978). The Images of Occupational Prestige. London, Macmillan.

Coxon, A.P.M. and Jones, C.L. (1979). Class and Hierarchy: The Social Meaning of Occupations. London, Macmillan.

Coxon, A.P.M. and Jones, C.L. (1979). Measurement and Meaning: Techniques and Methods of Studying Occupational Cognition. London, Macmillan.

Crompton, R. (2008). Class and Stratification. Cambridge, Polity Press. Cronbach, L.J. and Meehl, P.E. (1955). Construct Validity in Psychological Tests.

Psychological Bulletin, 52, 281-302. De Luca, D., Meraviglia, C. and Ganzeboom, H. (2010). ICAM: The International CAMSIS

Scale. International Research Association, Research Committee 28 Conference, Haifa. Elias, P. (1997). Occupational Classification (ISCO-88): Concepts, Methods, Reliability,

Validity and Cross-National Comparability. OECD Labour Market and Social Policy Occasional Papers No. 20, OECD Publishing.

24

Elias, P., Halstead, K. and Prandy, K. (1993). Computer Assisted Standard Occupational Classification. London, HMSO.

Elias, P. and McKnight, A. (2001). Skill measurement in official statistics: recent developments in the UK and the rest of Europe. Oxford Economic Papers, New Series 53 (3), 508-540.

Erikson, R. (1984). Social Class of Men, Women and Families. Sociology, 18 (4), 500-514. Erikson, R., Goldthorpe, J. and Portocarero, L. (1979). Intergenerational Class Mobility in

Three Western Societies: England, France, and Sweden. British Journal of Sociology, 30 (4), 415-441.

Erikson, R. and Goldthorpe, J.H. (1992). The Constant Flux: A Study of Class Mobility in Industrial Societies. Oxford, Clarendon.

Erikson, R. and Goldthorpe, J.H. (2010). Has social mobility in Britain decreased? Reconciling divergent findings on income and class mobility. The British journal of sociology, 61 (2), 211-230.

Erikson, R., Goldthorpe, J.H. and Hällsten, M. (2012). No way back up from ratcheting down? A critique of the ‘microclass’ approach to the analysis of social mobility. Acta Sociologica, 55 (3), 211-229.

Evans, G. (1992). Testing the Validity of the Goldthorpe Class Schema. European Sociological Review, 8 (3), 211-232.

Evans, G. and Mills, C. (1998). Identifying Class Structure: A Latent Class Analysis of the Criterion-Related and Construct Validity of the Goldthorpe Class Schema. European Sociological Review, 14 (1), 87-106.

Evans, G. and Mills, C. (2000). In search of the wage-labour/service contract: new evidence on the validity of the Goldthorpe class schema. British Journal of Sociology, 51 (4), 641-661.

Gabriel, P.E. and Schmitz, S. (2007). Gender differences in occupational distributions among workers. Monthly Lab. Rev. 130, 19.

Ganzeboom, H.B. (2010). A new international socio-economic index (ISEI) of occupational status for the international standard classification of occupation 2008 (ISCO-08) constructed with data from the ISSP 2002-2007. Annual Conference of International Social Survey Programme, Lisbon.

Ganzeboom, H.B., De Graaf, P.M. and Treiman, D.J. (1992). A standard international socio-economic index of occupational status. Social science research, 21 (1), 1-56.

Ganzeboom, H.B.G. (1996). Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification of Occupations. Social Science Research, 25, 201-239.

Ganzeboom, H.B.G. and Treiman, D.J. (1996). Internationally Comparable Measures of Occupational Status for the 1988 International Standard Classification of Occupations. Social Science Research, 25 (3), 201-235.

Ganzeboom, H.B.G. and Treiman, D.J. (2003). “Three Internationally Standardised Measures for Comparative Research on Occupational Status.” In Hoffmeyer-Zlotnik, J.H.P. and Wolf, C. Advances in Cross-National Comparison: A European Working Book for Demographic and Socio-Economic Variables. New York, Kluwer Academic, 159-193.

Gayle, V. and Lambert, P. (2011). An analysis of detailed parental occupational differences and their effects on children's school attainment in Britain. ISA Research Committee 28 Spring Meeting. University of Essex.

Giddens, A. (1981). The Class Structure of the Advanced Societies. London, Hutchinson. Glass, D.V. (1954). Social Mobility in Britain. London, Routledge. Goldthorpe, J. (1980). Social Mobility and Class Structure in Modern Britain. Oxford,

Clarendon.

25

Goldthorpe, J. (2012). Back to Class and Status: Or Why a Sociological View of Social Inequality Should Be Reasserted. Revista Espanola de Investigaciones Sociologicas, 137, 201-216.

Goldthorpe, J., Llewellyn, C. and Payne, C. (1980). Social Mobility and Class Structure in Modern Britain. Oxford, Oxford University Press.

Goldthorpe, J., Llewellyn, C. and Payne, C. (1987). Social Mobility and Class Structure in Modern Britain. Oxford, Clarendon Press.

Goldthorpe, J. and Marshall, G. (1992). The Promising Future of Class Analysis: A Response to Recent Critiques. Sociology, 26 (3), 381-400.

Goldthorpe, J.H. and McKnight, A. (2006). “The economic basis of social class.” In Morgan, S.L., Grusky, D.B. and Fields, G.S. Mobility and Inequality: Frontiers of Research in Sociology and Economics. Stanford University Press, 109-136.

Grusky, D.B., Jonsson, J.O., Di Carlo, M. and Pollack, R. (2012). The Microclass Project Homepage. Retrieved 12/12/2013, from http://www.classmobility.org/.

Grusky, D.B. and Sørensen, J.B. (1998). Can class analysis be salvaged? American Journal of Sociology, 103 (5), 1187-1234.

Grusky, D.B. and Sørensen, J.B. (2001). “Are there big social classes?” In Grusky, D.B. Social Stratification: Class, Race, and Gender in Sociological Perspective. Boulder, Westview Press, 183-194.

Grusky, D.B. and Weeden, K.A. (2006). “Does the sociological approach to studying social mobility have a future?” In Morgan, S.L., Grusky, D.B. and Fields, G.S. Mobility and Inequality. Stanford, Stanford University Press.

Harrison, E. and Rose, D. (2006). The European Socio-economic Classification (ESeC) User Guide. Colchester, University of Essex.

Hauser, R.M. and Warren, J.R. (1997). Socioeconomic indexes for occupations: A review, update, and critique. Sociological Methodology, 27, 177-298.

Hochstadt, S. (1999). Mobility and modernity: migration in Germany, 1820-1989, University of Michigan Press.

Holton, R.J. and Turner, B.S. (1989). “Has Class Analysis a Future? Max Weber and the challenge of Liberalism to Gemeinschaftlich accounts of class.” In Holton, R.J. and Turner, B.S. Max Weber on Economics and Society. London, Routledge, 160-196.

Hout, M. and DiPrete, T.A. (2006). What Have We Learned? RC28's Contributions To Knowledge About Social Stratification. Research in Social Stratification and Mobility, 24, 1-20.

Hout, M. and Hauser, R.M. (1992). Symmetry and hierarchy in social mobility: a methodological analysis of the CASMIN model of class mobility. European Sociological Review, 8, 239-266.

International Labour Organization. (2010). International Standard Classification of Occupations. Retrieved 12/12/2013, from

http://www.ilo.org/public/english/bureau/stat/isco/index.htm. Jarvis, S. and Jenkins, S.P. (1997). Low income dynamics in 1990s Britain. Fiscal Studies, 18,

123-143. Jencks, C., Perman, L. and Rainwater, L. (1988). What Is a Good Job? A New Measure of

Labor-Market Success. American Journal of Sociology, 93 (6), 1322-1357. Jenkins, S. and Van Kerm, P. (2009). “The Measurement of Economic Inequality.” In

Salverda, W., Nolvan, B. and Smeeding, T. Economic Inequality. Oxford, Oxford University Press, 40-69.

Jones, R. (2004). CASCOT (Computer Aided Structured COding Tool). Warwick, University of Warwick.

26

Jonsson, J.O., Grusky, D.B., Di Carlo, M., Pollack, R. and Brinton, M.C. (2009). Microclass Mobility: Social Reproduction in Four Countries. American Journal of Sociology, 114 (4), 977-1036.

Joyce, P. (1995). Class. Oxford, Oxford University Press. Kingston, P.W. (1994). “Are There Classes in the United States?” In Althauser, R. and

Wallace, M. Research in Social Stratification and Mobility Volume 13. Greenwich, JAI Press, 3-41.

Kunst, A.E. and Roskam, A.J. (2010). “Using the ESeC to describe socio-economic inequalities in health in Europe.” In Rose, D. and Harrison, E. Social Class in Europe: An introduction to the European Socio-economic Classification. London, Routledge.

Lambert, P. (2002). Handling occupational information. Building Research Capacity, 4, 9-12. Lambert, P. and Bihagen, E. (2012). “Stratification Research and Occupation-Based Social

Classifications.” In Lambert, P., Connelly, R.M., Blackburn, R.M. and Gayle, V. Social Stratification: Trends and Processes. Farnham, Ashgate, 13-28.

Lambert, P. and Griffiths, D. (2013). Seven new social classes? Retrieved 12/12/2013, from http://pullingapartproject.wordpress.com/2013/04/04/seven-new-social-classes/.

Lambert, P., Tan, L., Prandy, K., Gayle, V. and Bergman, M.M. (2008). The importance of specificity in occupation-based social classifications. International Journal of Sociology and Social Policy, 28 (5/6), 179-192.

Lambert, P.S. and Bihagen, E. (2014). Using occupation-based social classifications. Work, Employment & Society, 28 (3), 481-494.

Lee, D.J. and Turner, B.S. (1996). Conflicts about Class: Debating Inequality in Late Industrialism. London, Longman.

Levine, J.H. and Spadaro, J. (1988). “Occupational mobility: A structural Model.” In Wellman and Berkowitz, S.D. Social Structures: A Network Approach. Cambridge, Cambridge University Press.

Martin, J. and Roberts, C. (1984). Women and Employment: A lifetime perspective. London, HMSO.

Mills, C. (2007). “Unequal Jobs: Job quality and job satisfaction.” In McGovern, P., Hill, S., Mills, C. and White, M. Market, Class and Employment. Oxford, Oxford University Press, 233-282.

Mills, C. (2013). The Great British Class Fiasco. Retrieved 12/12/2013, from http://oxfordsociology.blogspot.co.uk/2013/04/the-great-british-class-fiasco.html. Mills, C. (2014). The Great British Class Fiasco: A Comment on Savage et al. Sociology, doi:

0038038513519880. Mitchell, M. (2010). Data Management Using Stata: A Practical Handbook. College Station,

Stata Press. Office for National Statistics. (2010). Standard Occupational Classification 2010.

Basingstoke, Palgrave Macmillan. Pahl, R.E. (1989). Is the Emperor Naked? Some Questions on the Adequacy of Sociological

Theory in Urban and Regional Research. International Journal of Urban and Regional Research, 13, 709-720.

Pakulski, J. and Waters, M. (1996). The Death of Class. Theory and Society, 25, 667-691. Parkin, F. (1971). Class Inequality and Political Order: Social Stratification in Capitalist and

Communist Societies. New York, Praeger. Payne, G. (2013). Models of Contemporary Social Class: The Great British Class Survey.

Methodological Innovations Online, 8 (1), 3-17. Penn, R.D. (1981). The Nuffield Class Categorisation. Sociology, 15 (2), 265-271. Pevalin, D. and Rose, D. (2002). The national statistics socio-economic classification: unifying

official and sociological approaches to the conceptualisation and measurement of social class in the united kingdom. Presses de Sciences Po.

27

Platt, L. (2011). Understanding inequalities: stratification and difference, Polity. Prandy, K. (1986). “Similarities of Life-Style and the Occupations of Women.” In Crompton,

R. and Mann, M. Gender and Stratification. Cambridge, Polity Press, 137-153. Prandy, K. (1990). The revised Cambridge scale of occupations. Sociology, 24, 629-655. Prandy, K. (1999). The Social Interaction Approach to the Measurement and analysis of

Social Stratification. International Journal of Sociology and Social Policy, 19, 204-236. Rollock, N. (2014). Race, Class and ‘The Harmony of Dispositions’. Sociology, doi:

0038038514521716. Rose, D. (1995). Social Research Update: Official Social Classification in the UK. Retrieved

12/12/2013, from http://sru.soc.surrey.ac.uk/SRU9.html. Rose, D. and Harrison, E. (2007). The European Socio-economic Classification: A New Social

Class Scheme for Comparative European Research. European Societies, 9 (3), 459-490.

Rose, D. and Harrison, E. (2010). Social Class in Europe: An Introduction to the European Socio-economic Classification. London, Routledge.

Rose, D. and Harrison, E. (2013). The Guardian: Letters. Little solidarity over the question of social class. from http://www.theguardian.com/society/2013/apr/05/solidarity-question-social-class.

Rose, D. and Pevalin, D.J. (2003). “The NS-SEC Explained.” In Rose, D. and Pevalin, D.J. A Researcher's Guide to the National Statistics Socio-economic Classification. London, Sage, 28-43.

Savage, M., Devine, F., Cunningham, N., Taylor, M., Li, Y., Hjellbrekke, J., Le Roux, B., Friedman, S. and Miles, A. (2013). A new model of social class: Findings from the BBC's Great British Class Survey Experiment. Sociology, 47 (2), 219-250.

Sayer, A. (2005). The Moral Significance of Class. Cambridge, Cambridge University Press. Shavit, Y. and Blossfeld, H.P. (1993). Persistent Inequality: Changing Educational Attainment

in Thirteen Countries. Social Inequality Series, ERIC. Skeggs, B. (1997). “Classifying Practices: Representations, Capitals and Recognitions.” In

Mahony, P. and Zmroczek, C. Class Matters. London, Taylor & Francis. Sobek, M. (1995). The comparability of occupations and the generation of income scores.

Historical Methods, 28 (1), 47-51. Sorokin, P. (1927). Social and Cultural Mobility. New York, Free Press. Stacey, M. (1969). Comparability in Social Research. London, Heinemann. Stewart, A., Prandy, K. and Blackburn, R.M. (1980). Social Stratification and Occupations.

New York, Holmes and Meier Publishers. Szreter, S.R.S. (1984). The genesis of the Registrar-General: social classification of

occupations. British Journal of Sociology, 35 (4), 522-546. Tahlin, M. (2007). “Skills and Wages in European Labour Markets.” In Gallie, D. Employment

Regimes and The Quality of Work. Oxford, Oxford University Press. Tampubolon, G. and Savage, M. (2012). “Intergenerational and Intragenerational Social

Mobility in Britain.” In Lambert, P.S., Connelly, R., Blackburn, M. and Gayle, V. Social Stratification: Trends and Processes. Aldershot, Ashgate, 115-131.

Taylor, M.F., Brice, J., Buck, N. and Prentice-Lane, E. (2010). British Household Panel Survey User Manual Volume A: Introduction, Technical Report and Appendices. Colchester, University of Essex.

Treiman, D.J. (1977). Occupational Prestige in Comparative Perspective. New York, Academic Press.

Treiman, D.J. (2009). Quantitative Data Analysis: Doing Social Research to Test Ideas. San Francisco, John Wiley & Sons.

28

Western, M. and Wright E.O. (1994). The Permeability of Class Boudaries to Intergenerational Mobility among Men in the United States, Canada, Norway, and Sweden. American Sociological Review, 59, 606-629.

Wright, E.O. (1989). The Debate on Classes. Verso, London. Wright, E.O. (1997). Class Counts. Cambridge, Cambridge University Press. Wright, E.O. (2005). Approaches to Class Analysis. Cambridge, Cambridge University Press. Wright, E.O. and Cho, D. (1992). The Relative Permeability of Class Boundaries to Cross-Class

Friendships: A Comparative Study of the United States, Canada, Sweden, and Norway. American Sociological Review, 57, 85-102.

Wright, E.O., Costello, C., Hachen, D. and Sprague, J. (1982). The American Class Structure. American Sociological Review, 47, 709-726.

Wright, E.O. and Martin, B. (1987). The Transformation of the American Class Structure, 1960-1980. American Sociological Review, 93, 1-29.

Wright, E.O. and Perrone, L. (1977). Marxist class categories and income inequality. American Sociological Review, 32-55.

Zijdeman, R.L. (2009). Like my father before me: Intergenerational occupational status transfer during industrialization (Zeeland, 1811–1915). Continuity and Change, 24 (03), 455-486.

ESRC Centre for Population Change • Working Paper 60 • January 2015ISSN 2042-4116

Improving our understanding of the key drivers and implications of population change

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